Analysis and characterization of cloud workloads provides crucial information for designing optimal resource management policies. In this work, we propose to analyse long range dependence nature of cloud resource workloads. Long range dependence is a phenomenon widely studied in Ethernet and Internet traffic. But there is a dearth of works that analyse long range dependence in cloud workloads. In this work, we propose to verify the presence of long range dependence in cloud workloads using autocorrelation analysis and rescaled range analysis method. In addition to experimental evidence, studies on long range dependence are incomplete without a sound theoretical justification in support of its origins in cloud workloads. In this context, we propose to analytically analyse, aggregate workload in the datacenter using different metrics like arrival, service distributions of jobs and their resource usage. For a dependable explanation of long range dependence in cloud workloads, we analyse workloads from standard real dataset of Google cluster trace. Based on the analysis, we see that analysed metrics display heavy tailed behaviour and using a mathematical formulation, we prove that aggregate workload exhibits long range dependence.

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云服务器的长期依赖：基于谷歌工作负载追踪的统计分析

云工作负载的分析和表征为设计最佳资源管理策略提供了关键信息。在这项工作中，我们建议分析云资源工作负载的长期依赖性质。远程依赖是以太网和互联网流量中广泛研究的一种现象。但是分析云工作负载的长期依赖的工作很少。在这项工作中，我们建议使用自相关分析和重新调整范围分析方法来验证云工作负载中远程依赖的存在。除了实验证据外，如果没有合理的理论依据来支持其起源于云工作负载，则对远程依赖的研究是不完整的。在这种情况下，我们建议使用不同的指标（如到达、作业的服务分布及其资源使用情况。为了可靠地解释云工作负载的长期依赖性，我们分析了来自 Google 集群跟踪的标准真实数据集的工作负载。基于分析，我们看到分析的指标显示重尾行为，并使用数学公式证明聚合工作负载表现出长期依赖性。